Adult_Census_Income_Project

Repo for the Adult Census Income Project

Project Overview

• Exploratory Data Analysis, outliers identification and data cleaning.

• Modelling using KNeighbors, Logistic Regression, Random Forest, CatBoost amd XGBoost classifiers.

• Hyperparameters tuning usings RandomizedSearchCV amd GridSearchCV.

• Metrics evaluation and Feature Importance.

Code and Resourses used

Python Version: 3.8.2

Packages: Pandas, Numpy, Matplotlib, Seaborn, SKlearn, XGBoost, CatBoost

EDA: Exploratory Data Analysis

The EDA shows distribution of data and relation between different features’ Below are few highlights from the graphs:

Data Cleaning

Create a preprocess_data(df) function that performs transformations on the DataFrame given as parameter and returns its converted version. Below the changes function makes:

• Fill missing numerical values with feature median
• Convert Object data into numerical

Model Building

• Split Data into train and test data
• Create fit_and_score(model) function to instantiate and compare accuracy from different estimators simultaneously.
• Initially 5 different models:
  KNeighbors Classifier
  Logistic Regression
  Random Forest Classifier
  XGBoost Classifier
  CatBoost Classifier
• Hyperparameter tuning using RandomizedSearchCV and GridSearchCV for the two best performant classifiers.

Model Performance

• Metrics evaluation using Cross Validation (Precision, Recall and F1 scores), ROC curve and AUC, Confusion Matrix and Classification Report

• Feature Importance